Ensuring metadata and index consistency using write intents

ABSTRACT

A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method begins by, receiving a write data object request and writing and committing the data object as a set of encoded data slices into DSN memory. The method continues by writing and committing an index consistency write-intent to DSN memory. The method continues by writing metadata of the data object to DSN memory. The method continues by write and committing an index entry to DSN memory. The method continues, during a finalization of the index consistency write-intent, by executing the index consistency write-intent to ensure consistency between the metadata of the data object and metadata located in the index entry.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9A is a schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents in accordance withthe present invention;

FIG. 9B is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents in accordance withthe present invention;

FIG. 9C is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents in accordance withthe present invention;

FIG. 9D is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents in accordance withthe present invention;

FIG. 9E is a logic diagram of an example of ensuring metadata and indexconsistency using write intents in accordance with the presentinvention; and

FIG. 9F is a logic diagram of an example of ensuring metadata and indexconsistency using write intents in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (TO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5 Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In embodiments of DSN memory described herein, “Eventual ConsistencyIntents” are small objects that can be stored in a DSN memory to ensurethat a piece of work eventually gets done (i.e., does not have to beperformed immediately). A DSN processing unit will generally write theseintents along with an immediate consistency update, and return aresponse to the user before moving on to processing the eventualconsistency update. For example, a check of potential encoded data slicenames within a range of slice names includes common encoded data sliceidentifiers consistent with a particular pillar of a vault. Once the DSNprocessing unit finishes the eventual consistency update in thebackground, it deletes the intent from the DSN memory. If, however, theDSN processing unit crashes or runs into a network partition whichprevents it from accomplishing the update, the intent will be left inDSN memory.

FIG. 9A is a schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents. When performing adata object write to DSN memory 22, a processing module (e.g., client DSmodule 34) identifies storage units (e.g., storage units 36-1, 36-2,36-3, 36-N, etc.) where encoded data slices of the data object will bewritten. For each encoded data slice of a data segment, the grid module(FIGS. 3-5) generates a unique slice name and attaches it thereto. Theslice name includes a universal routing information field and a vaultspecific field and may be 48 bytes (e.g., 24 bytes for each of theuniversal routing information field and the vault specific field). Theuniversal routing information field includes a slice index 908, a vaultID, a vault generation, and a reserved field. The slice index 908 isbased on the pillar number and the vault ID and, as such, is unique foreach pillar (e.g., slices of the same pillar for the same vault for anysegment will share the same slice index). The vault specific fieldincludes a data name, which includes a file ID and a segment number(e.g., a sequential numbering of data segments 1-Y of a simple dataobject or a data block number). In addition to the slice index, metadataassociated with the encoded data slices can include control information,such as size, type, date of last change, version, etc. of the encodeddata slices.

As shown, a dispersed storage network (DSN) 10 includes distributedstorage and task (DST) processing unit 16 (computing device) of FIG. 1,the network 24 of FIG. 1, and a set of storage units 36 (1-N) within DSNmemory 22. The DST processing unit 16 includes the DST client module 34of FIG. 1 and memory 54. The DSN functions to maintain integrity ofindexes of stored data objects stored as a plurality of sets of encodeddata slices 902 in the set of storage units SUS 36 (1-N) within DSNmemory 22.

In an example of operation of maintaining of the integrity of theindexes, the DST client module 34 determines to store a data object(write data object) as encoded data slices in the set of storage units36 (1-N). During a write operation, encoded data slices are sent to DSNmemory 22 with an exclusive “write lock” on the slice name to preventothers from updating that slice name during the duration of the lock.For example, the DST client module 34 receives a store data request 900.Having determined to store the object, the DST client module 34generates index consistency write-intent information 906 in order tocheck consistency of the index at a later time.

The index consistency write-intent information element specifies anintention that a DSN processing unit wants a consistent index withmatching metadata to achieve for a write of a data object to DSN memory.At the completion of a data object write, the DS processing unit 16creates or updates a unique metadata 904 and proposed index entry 908for the data object. Metadata 904 may include one or more of: a sizeindicator of the data object, a number of regions of the data object, aregion size indicator, a number of data segments per region, a slicename range, a source name associated with the identifier of the dataobject, the identifier of the data object, an identifier of a requestingentity, a current timestamp, and an estimated time to completion of thestoring.

The DS processing unit will then write the metadata 904 and an indexconsistency write-intent 906 in a single transaction to DSN memory andretry on a check or conflict exception. The DS processing unit will alsocommit both the data object and index consistency write-intent 905. Inone embodiment, the write and commit data object content and write andcommit index consistency intent are processed substantially in parallel,although they may not be completed simultaneously. A commit providesvisibility to the written encoded data slices for other readers of theDS memory, releases exclusive “write lock” on associated slice name andallows other writers to the storage units to update the slice name.

FIG. 9B is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents. After the metadatais written, a proposed index entry 908 is written to DSN memory 22 andthereafter committed 910, thereby creating the index entry 908 in DSNmemory. After creation of the index entry, the data object metadata iscommitted 912.

FIG. 9C is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents. After the commit ofthe proposed index entry and data object metadata, the user (write datarequestor) would be notified of a write success 916 (data object writesuccess and the index consistency intent would be deleted from DSNmemory 22 (write and commit) 914.

FIG. 9D is another schematic block diagram of an embodiment of ensuringmetadata and index consistency using write intents. If, duringprocessing of the data object, metadata and/or index, a failure or crashoccurs, a cleanup agent process (FIG. 9F) is used to maintainconsistency. DST processing unit 16 reads/writes (R/W) metadata 918 ofthe data object to generate an index entry to be written to DSN memoryor possible be deleted from DSN memory (discussed further hereafter) andthereafter to lock it. DST processing unit then writes and commits theindex entry to index 920 (or deletes the index entry if the metadatadidn't exist). For example, asynchronously, during a finalization ofindex consistency intent 906, the index consistency write-intent wouldbe executed to ensure consistency (correlation) between the data objectmetadata and metadata located in the index entry. If, during theprocessing of the index consistency write-intent, a differing metadata(different than metadata 904) was encountered, or a differing expectedrevision (unexpected revision), the cleanup processing agent (associatedwith DST processing unit 16 or optionally associated with integrityprocessing unit 20) would handle the discrepancy by rolling back themetadata 922, otherwise the index consistency intent 906 wouldself-destruct (be deleted 914) as no action was required. In oneembodiment, rolling back the metadata unlocks encoded data slices thatwere written with a “write” and rolls back changes made by that “write”.

FIG. 9E is a flowchart illustrating an example diagram of ensuringmetadata and index consistency using index consistency intents. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-2,3-8, and also FIGS. 9A-9F. The method includes step 924, where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule 34) receives a write data object request (900) and writes andcommits the data object as a set of encoded data slices into DSN memory22 (storage units (SUs) 36-1 through 36-N). In parallel, the methodcontinues at step 926, where DST processing unit then writes and commitsthe index consistency write-intent. If the data object and indexconsistency write-intent write and commit steps are not successfullycompleted (committed), cleanup agent process (A), as shown in FIG. 9F,is used to maintain consistency.

If the data object and index consistency write-intent write and commitsteps are successfully completed (committed), the method continues atstep 928, where the processing module writes the data object metadata toDSN memory 22. If the write data object metadata step is notsuccessfully completed (stored in DSN memory 22), cleanup agent process(A), as shown in FIG. 9F, is used to maintain consistency.

If the write data object metadata step is successfully completed (storedin DSN memory 22), the method continues at step 930, where theprocessing module generates (writes) an index entry to be written to DSNmemory 22 and thereafter locks it (commits). If the write index entrystep is not successfully completed (stored/committed in DSN memory 22),cleanup agent process (A), as shown in FIG. 9F, is used to maintainconsistency.

If the write index entry step is successfully completed(stored/committed in DSN memory 22), the method continues at step 932,where the processing module commits the data object metadata to DSNmemory 22. If the committing of the data object metadata to DSN memory22 step is not successfully completed (stored/committed in DSN memory22), cleanup agent process (A), as shown in FIG. 9F, is used to maintainconsistency.

If the committing of the data object metadata to DSN memory 22 step issuccessfully completed (stored/committed in DSN memory 22), the methodcontinues at step 934, where the processing module deletes the indexconsistency write-intent and, in parallel, in step 936, notifies a writedata requestor (e.g., user) of a successful data object write.

FIG. 9F is a flowchart illustrating an example diagram of ensuringmetadata and index consistency using index consistency intents. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-2,3-8, and also FIGS. 9A-9F. The method includes step 938, where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule 34) reads the data object metadata to generate an index entry tobe written to DSN memory or possibly be deleted from DSN memory.

The method continues at step 940, where the processing module writes thedata object metadata to DSN memory 22 to lock it (e.g., slice nameprotected from other uses). The method continues at step 942, where theprocessing module writes and commits the index entry to index (ordeletes the index entry if the metadata didn't exist). For example,asynchronously, during a finalization of index consistency intent 906,the index consistency write-intent would be executed to ensureconsistency (correlation) between the data object metadata and metadatalocated in the index entry. If, during the processing of the indexconsistency write-intent, a differing metadata (different than metadata904) was encountered, or a differing expected revision, the cleanupprocessing agent (associated with DST processing unit 16 or optionallyassociated with integrity processing unit 20) would handle thediscrepancy by rolling back the metadata in step 944, otherwise, in step946, the index consistency intent 906 would self-destruct (be deleted)as no action was required.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other computing devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of the dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: receiving a write data object request;writing and committing the data object as a set of encoded data slicesinto DSN memory; writing and committing an index consistencywrite-intent to DSN memory; writing metadata of the data object to DSNmemory; writing and committing an index entry to DSN memory; committingthe metadata of the data object to DSN memory; deleting the indexconsistency write-intent; and notifying a write data object requestor ofa successful data object write.
 2. The method of claim 1 furthercomprises, if during processing of the index consistency write-intent,metadata different from the metadata of the data object was encountered,determining that a discrepancy has occurred.
 3. The method of claim 1further comprises, if during processing of the index consistencywrite-intent, metadata different from the metadata of the data objectwas encountered, rolling back the metadata.
 4. The method of claim 1further comprises, if during processing of the index consistencywrite-intent, an unexpected revision value was encountered, determiningthat a discrepancy has occurred.
 5. The method of claim 1 furthercomprises, if during processing of the index consistency write-intent,an unexpected revision value was encountered, rolling back the metadata.6. The method of claim 1, wherein during a finalization of the indexconsistency write-intent, the index consistency write-intent would beexecuted to ensure consistency between the metadata of the data objectand metadata located in the index entry.
 7. The method of claim 1further comprises, when the writing and committing the data object as aset of encoded data slices into DSN memory is unsuccessful, cleanupprocessing including: reading the metadata of the data object togenerate an index entry to be written to DSN memory; writing themetadata of the data object to the DSN memory to lock it; writing andcommitting the index entry to an index; rolling back the metadata; anddeleting the index consistency write-intent.
 8. The method of claim 1further comprises, when the writing and committing an index consistencywrite-intent to DSN memory is unsuccessful, cleanup processingincluding: reading the metadata of the data object to generate an indexentry to be written to DSN memory; writing the metadata of the dataobject to the DSN memory to lock it; writing and committing the indexentry to an index; rolling back the metadata; and deleting the indexconsistency write-intent.
 9. The method of claim 1 further comprises,when the writing metadata of the data object to DSN memory isunsuccessful, cleanup processing including: reading the metadata of thedata object to generate an index entry to be written to DSN memory;writing the metadata of the data object to the DSN memory to lock it;writing and committing the index entry to an index; rolling back themetadata; and deleting the index consistency write-intent.
 10. Themethod of claim 1 further comprises, when the writing and committing anindex entry to DSN memory is unsuccessful, cleanup processing including:reading the metadata of the data object to generate an index entry to bewritten to DSN memory; writing the metadata of the data object to theDSN memory to lock it; writing and committing the index entry to anindex; rolling back the metadata; and deleting the index consistencywrite-intent.
 11. The method of claim 10, wherein the index entry isdeleted if the metadata of the data object was not located in the DSNmemory.
 12. The method of claim 1 further comprises, when the committingthe metadata of the data object to DSN memory is unsuccessful, cleanupprocessing including: reading the metadata of the data object togenerate an index entry to be written to DSN memory; writing themetadata of the data object to the DSN memory to lock it; writing andcommitting the index entry to an index; rolling back the metadata; anddeleting the index consistency write-intent.
 13. A computing device of agroup of computing devices of a dispersed storage network (DSN), thecomputing device comprises: an interface; a local memory; and aprocessing module operably coupled to the interface and the localmemory, wherein the processing module functions to: receive a write dataobject request; write and committing the data object as a set of encodeddata slices into DSN memory; write and committing an index consistencywrite-intent to DSN memory; write metadata of the data object to DSNmemory; write and committing an index entry to DSN memory; commit themetadata of the data object to DSN memory; delete the index consistencywrite-intent; and notify a write data object requestor of a successfuldata object write.
 14. The computing device of claim 13 furthercomprises, if during processing of the index consistency write-intent,metadata different from the metadata of the data object was encountered,determining that a discrepancy has occurred.
 15. The computing device ofclaim 13 further comprises, if during processing of the indexconsistency write-intent, metadata different from the metadata of thedata object was encountered, rolling back the metadata.
 16. Thecomputing device of claim 13, wherein during a finalization of the indexconsistency write-intent, the index consistency write-intent would beexecuted to ensure consistency between the metadata of the data objectand metadata located in the index entry.
 17. The computing device ofclaim 13, wherein the index consistency write-intent is written in asingle transaction with the metadata of the data object.
 18. A methodfor execution by one or more processing modules of one or more computingdevices of a dispersed storage network (DSN), the method comprises:receiving a write data object request; writing and committing the dataobject as a set of encoded data slices into DSN memory; writing andcommitting an index consistency write-intent to DSN memory; writingmetadata of the data object to DSN memory; write and committing an indexentry to DSN memory; and wherein during a finalization of the indexconsistency write-intent, the index consistency write-intent would beexecuted to ensure consistency between the metadata of the data objectand metadata located in the index entry.
 19. The method of claim 18further comprises, if during finalization of the index consistencywrite-intent, metadata different from the metadata of the data objectwas encountered, rolling back the data object metadata.
 20. The methodof claim 18, wherein the index consistency write-intent is written in asingle transaction with the metadata of the data object.